Foreground extraction of a video refers to separating a foreground object in the video from a background. As an increasingly common video type, surveillance video has the characteristics of long time, complex foreground and increasing resolution. In real life, surveillance videos are necessary for analysis of some cases and accidents since the surveillance videos can provide more evidence of the cases.
Existing foreground extraction technologies mainly use a Gaussian mixture model, an expectation maximization (EM) algorithm, saliency detection, a Trimap algorithm, a GrabCut algorithm and a superpixel method to extract foreground objects. However, the video foreground extraction methods have the following disadvantages:
(1) the above methods can partly extract a salient region in a video, but the extraction process does not take into account the difference between a static foreground and a dynamic foreground, which leads to a globally uniform background, while a foreground object in the stationary state should be seen as a new background;
(2) for surveillance videos with an increasingly large size, the processing speed of the above methods is slower and slower, and the processing efficiency for large-size videos is low; and
(3) most of the above methods have high algorithm complexity and poor processing performance.